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Papinder Dosanjh, Platform team - data science and recommendations , ASOS

Path to power

 

I started my career in a start-up specialising in geo-location solutions for media, retail and telecoms clients. I joined dunnhumby in 2002, initially as a client manager, before moving up the ranks to senior product manager, responsible for developing a personalisation capability for Tesco group across its e-commerce, loyalty and CRM functions. In 2014, I moved to Accenture as digital transformation lead, delivering strategic initiatives across retail banking, telecoms, communications and media.
I then joined TUI in 2017 where my focus was on defining the global “one” analytics strategy for the executive board and championing an analytics centre of excellence. Now at ASOS, I’m responsible for executing the business and technology transformation needed to deliver our “AI everywhere” strategy. The focus of this is to embed AI and machine learning across all areas of our customer experience, and also to support the optimisation of back-end functions through data. This is supported by investment into in-house data science, big data and software engineering teams to deliver data products faster.

 

Talent and skills are always a challenge to find - how are you tackling this in your organisation?

 

We think we have a responsibility to help our own people develop. We have dedicated mentors and practice leads who are there to help people improve their skills, and each month we also host Tech Presents, a forum to share the projects everyone is working on, and Tech Develops, an entire day dedicated to professional development. In data, we build on these with regular “data talks” events and hands-on workshops to up-skill colleagues, including on big data engineering and data science to increase data literacy. These run alongside our tech-wide regular hackathons, including an upcoming one on AI and machine learning. On the recruitment side, we’re working to widen the technology (in particular data) talent pool, for example, by visiting schools and universities to encourage young women into tech and data. We are also creating dedicated teams focused on delivering end-to-end data products that use machine learning, natural language processing and computer vision, helping us share skills internally and make sure our teams own the overall data science workflow for their features.

 

What aspect of data, analytics or their use are you most optimistic about and why?

 

I’m optimistic about the unique customer experiences we can create through data and analytics. For example, we use our in-house recommendation engine to surface millions of relevant, personalised product recommendations for more than 18 million customers globally and we’re working on a number of exciting ways to develop this further.

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